On the origin of the cumulative semantic inhibition effect

We report an extension of the cumulative semantic inhibition effect found by Howard, Nickels, Coltheart, and Cole-Virtue (2006). Using more sensitive statistical analyses, we found a significant variation in the magnitude of the effect across categories. This variation cannot be explained by the naming speed of each category. In addition, using a sub-sample of the data, a second cumulative effect arouse for newly-defined supra-categories, over and above the effect of the original ones. We discuss these findings in terms of the representations that drive lexical access, and interpret them as supporting featural or distributed hypotheses

Sensorimotor semantics on the spot: brain activity dissociates between conceptual categories within 150 ms

Although semantic processing has traditionally been associated with brain responses maximal at 350–400 ms, recent studies reported that words of different semantic types elicit topographically distinct brain responses substantially earlier, at 100–200 ms. These earlier responses have, however, been achieved using insufficiently precise source localisation techniques, therefore casting doubt on reported differences in brain generators. Here, we used high-density MEG-EEG recordings in combination with individual MRI images and state-of-the-art source reconstruction techniques to compare localised early activations elicited by words from different semantic categories in different cortical areas. Reliable neurophysiological word-category dissociations emerged bilaterally at ~ 150 ms, at which point action-related words most strongly activated frontocentral motor areas and visual object-words occipitotemporal cortex. These data now show that different cortical areas are activated rapidly by words with different meanings and that aspects of their category-specific semantics is reflected by dissociating neurophysiological sources in motor and visual brain systems

On the origin of the "cumulative semantic inhibition" effect

International audienc

Trepan reloaded: A knowledge-driven approach to explaining black-box models

Explainability in Artificial Intelligence has been revived as a topic of active research by the need to demonstrate safety to users and gain their trust in the ahow' and awhy' of automated decision-making. Whilst a plethora of approaches have been developed for post-hoc explainability, only a few focus on how to use domain knowledge, and how it influences the understandability of global explanations from the users' perspective. In this paper, we show how to use ontologies to create more understandable post-explanations of machine learning models. In particular, we build on TREPAN, an algorithm that explains artificial neural networks by means of decision trees, and we extend it to TREPAN Reloaded by including ontologies that model domain knowledge in the process of generating explanations. We present the results of a user study that measures the understandability of decision trees through time and accuracy of responses as well as reported user confidence and understandability in relation to syntactic complexity of the trees. The user study considers domains where explanations are critical, namely finance and medicine. The results show that decision trees generated with our algorithm, taking into account domain knowledge, are more understandable than those generated by standard TREPAN without the use of ontologies

Using ontologies to enhance human understandability of global post-hoc explanations of black-box models

The interest in explainable artificial intelligence has grown strongly in recent years because of the need to convey safety and trust in the ‘how’ and ‘why’ of automated decision-making to users. While a plethora of approaches has been developed, only a few focus on how to use domain knowledge and how this influences the understanding of explanations by users. In this paper, we show that by using ontologies we can improve the human understandability of global post-hoc explanations, presented in the form of decision trees. In particular, we introduce TREPAN Reloaded, which builds on TREPAN, an algorithm that extracts surrogate decision trees from black-box models. TREPAN Reloaded includes ontologies, that model domain knowledge, in the process of extracting explanations to improve their understandability. We tested the understandability of the extracted explanations by humans in a user study with four different tasks. We evaluate the results in terms of response times and correctness, subjective ease of understanding and confidence, and similarity of free text responses. The results show that decision trees generated with TREPAN Reloaded, taking into account domain knowledge, are significantly more understandable throughout than those generated by standard TREPAN. The enhanced understandability of post-hoc explanations is achieved with little compromise on the accuracy with which the surrogate decision trees replicate the behaviour of the original neural network models

Motor cortex maps articulatory features of speech sounds

The processing of spoken language has been attributed to areas in the superior temporal lobe, where speech stimuli elicit the greatest activation. However, neurobiological and psycholinguistic models have long postulated that knowledge about the articulatory features of individual phonemes has an important role in their perception and in speech comprehension. To probe the possible involvement of specific motor circuits in the speech-perception process, we used event-related functional MRI and presented experimental subjects with spoken syllables, including [p] and [t] sounds, which are produced by movements of the lips or tongue, respectively. Physically similar nonlinguistic signal-correlated noise patterns were used as control stimuli. In localizer experiments, subjects had to silently articulate the same syllables and, in a second task, move their lips or tongue. Speech perception most strongly activated superior temporal cortex. Crucially, however, distinct motor regions in the precentral gyrus sparked by articulatory movements of the lips and tongue were also differentially activated in a somatotopic manner when subjects listened to the lip- or tongue-related phonemes. This sound-related somatotopic activation in precentral gyrus shows that, during speech perception, specific motor circuits are recruited that reflect phonetic distinctive features of the speech sounds encountered, thus providing direct neuroimaging support for specific links between the phonological mechanisms for speech perception and production

Stress neuropeptide levels in adults with chest pain due to coronary artery disease: potential implications for clinical assessment

: Substance P (SP) and neuropeptide Y (NPY) are neuropeptides involved in nociception. The study of biochemical markers of pain in communicating critically ill coronary patients may provide insight for pain assessment and management in critical care. Purpose of the study was to to explore potential associations between plasma neuropeptide levels and reported pain intensity in coronary critical care adults, in order to test the reliability of SP measurements for objective pain assessment in critical care